Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations45593
Missing cells8903
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 MiB
Average record size in memory539.3 B

Variable types

Text1
Numeric9
DateTime2
Categorical6

Alerts

Delivery_location_latitude is highly overall correlated with translogi_latitudeHigh correlation
Delivery_location_longitude is highly overall correlated with translogi_longitudeHigh correlation
Delivery_person_Age is highly overall correlated with Vehicle_conditionHigh correlation
Delivery_person_Ratings is highly overall correlated with Vehicle_conditionHigh correlation
Road_traffic_density is highly overall correlated with Temperature and 1 other fieldsHigh correlation
Temperature is highly overall correlated with Road_traffic_density and 1 other fieldsHigh correlation
Time_taken is highly overall correlated with Traffic_IndexHigh correlation
Traffic_Index is highly overall correlated with Road_traffic_density and 1 other fieldsHigh correlation
Vehicle_condition is highly overall correlated with Delivery_person_Age and 1 other fieldsHigh correlation
Weatherconditions is highly overall correlated with TemperatureHigh correlation
translogi_latitude is highly overall correlated with Delivery_location_latitudeHigh correlation
translogi_longitude is highly overall correlated with Delivery_location_longitudeHigh correlation
Delivery_person_Age has 1854 (4.1%) missing values Missing
Delivery_person_Ratings has 1908 (4.2%) missing values Missing
Time_Orderd has 1731 (3.8%) missing values Missing
Weatherconditions has 616 (1.4%) missing values Missing
Road_traffic_density has 601 (1.3%) missing values Missing
multiple_deliveries has 993 (2.2%) missing values Missing
City has 1200 (2.6%) missing values Missing
ID has unique values Unique
translogi_latitude has 3640 (8.0%) zeros Zeros
translogi_longitude has 3640 (8.0%) zeros Zeros

Reproduction

Analysis started2025-01-27 16:23:58.143946
Analysis finished2025-01-27 16:24:11.775401
Duration13.63 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

ID
Text

Unique 

Distinct45593
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2025-01-27T21:54:12.331573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9457812
Min length6

Characters and Unicode

Total characters316679
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45593 ?
Unique (%)100.0%

Sample

1st row0x4607
2nd row0xb379
3rd row0x5d6d
4th row0x7a6a
5th row0x70a2
ValueCountFrequency (%)
0x4607 1
 
< 0.1%
0x36b8 1
 
< 0.1%
0xb816 1
 
< 0.1%
0x6c6b 1
 
< 0.1%
0xd987 1
 
< 0.1%
0x5d6d 1
 
< 0.1%
0x7a6a 1
 
< 0.1%
0x70a2 1
 
< 0.1%
0x9bb4 1
 
< 0.1%
0x95b4 1
 
< 0.1%
Other values (45583) 45583
> 99.9%
2025-01-27T21:54:13.032215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 316679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 316679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 316679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 54129
17.1%
x 45593
14.4%
45593
14.4%
b 11995
 
3.8%
4 11890
 
3.8%
7 11886
 
3.8%
6 11874
 
3.7%
a 11865
 
3.7%
1 11824
 
3.7%
c 11816
 
3.7%
Other values (8) 88214
27.9%

Delivery_person_Age
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)0.1%
Missing1854
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean29.567137
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:13.117060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q125
median30
Q335
95-th percentile39
Maximum50
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8151554
Coefficient of variation (CV)0.19667631
Kurtosis-1.0583326
Mean29.567137
Median Absolute Deviation (MAD)5
Skewness0.018669335
Sum1293237
Variance33.816032
MonotonicityNot monotonic
2025-01-27T21:54:13.206927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
35 2262
 
5.0%
36 2260
 
5.0%
37 2227
 
4.9%
30 2226
 
4.9%
38 2219
 
4.9%
24 2210
 
4.8%
32 2202
 
4.8%
22 2196
 
4.8%
29 2191
 
4.8%
33 2187
 
4.8%
Other values (12) 21559
47.3%
ValueCountFrequency (%)
15 38
 
0.1%
20 2136
4.7%
21 2153
4.7%
22 2196
4.8%
23 2087
4.6%
24 2210
4.8%
25 2174
4.8%
26 2159
4.7%
27 2150
4.7%
28 2179
4.8%
ValueCountFrequency (%)
50 53
 
0.1%
39 2144
4.7%
38 2219
4.9%
37 2227
4.9%
36 2260
5.0%
35 2262
5.0%
34 2166
4.8%
33 2187
4.8%
32 2202
4.8%
31 2120
4.6%

Delivery_person_Ratings
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)0.1%
Missing1908
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.6337805
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:13.332114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.5
median4.7
Q34.9
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.33471641
Coefficient of variation (CV)0.07223398
Kurtosis15.670705
Mean4.6337805
Median Absolute Deviation (MAD)0.2
Skewness-2.4935516
Sum202426.7
Variance0.11203507
MonotonicityNot monotonic
2025-01-27T21:54:13.503926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.8 7148
15.7%
4.7 7142
15.7%
4.9 7041
15.4%
4.6 6940
15.2%
5 3996
8.8%
4.5 3303
7.2%
4.1 1430
 
3.1%
4.2 1418
 
3.1%
4.3 1409
 
3.1%
4.4 1361
 
3.0%
Other values (18) 2497
 
5.5%
(Missing) 1908
 
4.2%
ValueCountFrequency (%)
1 38
0.1%
2.5 20
< 0.1%
2.6 22
< 0.1%
2.7 22
< 0.1%
2.8 19
< 0.1%
2.9 19
< 0.1%
3 6
 
< 0.1%
3.1 29
0.1%
3.2 29
0.1%
3.3 25
0.1%
ValueCountFrequency (%)
6 53
 
0.1%
5 3996
8.8%
4.9 7041
15.4%
4.8 7148
15.7%
4.7 7142
15.7%
4.6 6940
15.2%
4.5 3303
7.2%
4.4 1361
 
3.0%
4.3 1409
 
3.1%
4.2 1418
 
3.1%

translogi_latitude
Real number (ℝ)

High correlation  Zeros 

Distinct657
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.017729
Minimum-30.905562
Maximum30.914057
Zeros3640
Zeros (%)8.0%
Negative431
Negative (%)0.9%
Memory size356.3 KiB
2025-01-27T21:54:13.655037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-30.905562
5-th percentile0
Q112.933284
median18.546947
Q322.728163
95-th percentile26.913987
Maximum30.914057
Range61.819619
Interquartile range (IQR)9.794879

Descriptive statistics

Standard deviation8.185109
Coefficient of variation (CV)0.48097541
Kurtosis3.713716
Mean17.017729
Median Absolute Deviation (MAD)5.482766
Skewness-1.3615831
Sum775889.3
Variance66.996009
MonotonicityNot monotonic
2025-01-27T21:54:13.816516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
26.911378 182
 
0.4%
26.914142 180
 
0.4%
26.892312 176
 
0.4%
26.90294 176
 
0.4%
26.902908 176
 
0.4%
26.88842 174
 
0.4%
26.905287 173
 
0.4%
26.913726 173
 
0.4%
22.308096 172
 
0.4%
Other values (647) 40371
88.5%
ValueCountFrequency (%)
-30.905562 1
 
< 0.1%
-30.902872 2
< 0.1%
-30.899584 3
< 0.1%
-30.895817 3
< 0.1%
-30.893384 1
 
< 0.1%
-30.893244 1
 
< 0.1%
-30.892978 1
 
< 0.1%
-30.890184 1
 
< 0.1%
-30.885915 1
 
< 0.1%
-30.885814 1
 
< 0.1%
ValueCountFrequency (%)
30.914057 42
0.1%
30.905562 37
0.1%
30.902872 32
0.1%
30.899992 38
0.1%
30.899584 41
0.1%
30.895817 36
0.1%
30.895204 41
0.1%
30.893384 38
0.1%
30.893244 38
0.1%
30.893234 39
0.1%

translogi_longitude
Real number (ℝ)

High correlation  Zeros 

Distinct518
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.231332
Minimum-88.366217
Maximum88.433452
Zeros3640
Zeros (%)8.0%
Negative162
Negative (%)0.4%
Memory size356.3 KiB
2025-01-27T21:54:13.999296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-88.366217
5-th percentile0
Q173.17
median75.898497
Q378.044095
95-th percentile85.325347
Maximum88.433452
Range176.79967
Interquartile range (IQR)4.874095

Descriptive statistics

Standard deviation22.883647
Coefficient of variation (CV)0.32583245
Kurtosis10.303039
Mean70.231332
Median Absolute Deviation (MAD)2.161724
Skewness-3.2201594
Sum3202057.1
Variance523.66131
MonotonicityNot monotonic
2025-01-27T21:54:14.159332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
75.789034 182
 
0.4%
75.805704 181
 
0.4%
75.793007 177
 
0.4%
75.806896 176
 
0.4%
75.792934 176
 
0.4%
75.75282 174
 
0.4%
75.800689 174
 
0.4%
75.794592 173
 
0.4%
73.167753 173
 
0.4%
Other values (508) 40367
88.5%
ValueCountFrequency (%)
-88.366217 1
 
< 0.1%
-88.352885 1
 
< 0.1%
-88.349843 1
 
< 0.1%
-88.322337 1
 
< 0.1%
-85.33982 1
 
< 0.1%
-85.335486 1
 
< 0.1%
-85.325731 3
< 0.1%
-85.325447 2
< 0.1%
-85.325146 1
 
< 0.1%
-85.3172 1
 
< 0.1%
ValueCountFrequency (%)
88.433452 35
0.1%
88.433187 36
0.1%
88.400581 34
0.1%
88.400467 33
0.1%
88.39331 36
0.1%
88.393294 38
0.1%
88.368628 35
0.1%
88.36783 33
0.1%
88.366217 33
0.1%
88.365507 37
0.1%

Delivery_location_latitude
Real number (ℝ)

High correlation 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.465186
Minimum0.01
Maximum31.054057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:14.299211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q112.988453
median18.633934
Q322.785049
95-th percentile27.023726
Maximum31.054057
Range31.044057
Interquartile range (IQR)9.796596

Descriptive statistics

Standard deviation7.335122
Coefficient of variation (CV)0.41998534
Kurtosis0.26434584
Mean17.465186
Median Absolute Deviation (MAD)5.47924
Skewness-0.70106646
Sum796290.22
Variance53.804015
MonotonicityNot monotonic
2025-01-27T21:54:14.442569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42266
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
31.054057 3
< 0.1%
31.045562 4
< 0.1%
31.044057 4
< 0.1%
31.042872 2
< 0.1%
31.039992 3
< 0.1%
31.039584 4
< 0.1%
31.035817 4
< 0.1%
31.035562 3
< 0.1%
31.035204 4
< 0.1%
31.033384 4
< 0.1%

Delivery_location_longitude
Real number (ℝ)

High correlation 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.845702
Minimum0.01
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:14.613607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q173.28
median76.002574
Q378.107044
95-th percentile85.375486
Maximum88.563452
Range88.553452
Interquartile range (IQR)4.827044

Descriptive statistics

Standard deviation21.118812
Coefficient of variation (CV)0.29809588
Kurtosis7.1044509
Mean70.845702
Median Absolute Deviation (MAD)2.196673
Skewness-2.9563849
Sum3230068.1
Variance446.00422
MonotonicityNot monotonic
2025-01-27T21:54:14.950037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42266
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
88.563452 2
< 0.1%
88.563187 4
< 0.1%
88.543452 3
< 0.1%
88.543187 4
< 0.1%
88.530581 4
< 0.1%
88.530467 3
< 0.1%
88.523452 4
< 0.1%
88.52331 4
< 0.1%
88.523294 2
< 0.1%
88.523187 2
< 0.1%
Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size356.3 KiB
Minimum2022-02-11 00:00:00
Maximum2022-04-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-27T21:54:15.068971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:15.191553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

Time_Orderd
Date

Missing 

Distinct176
Distinct (%)0.4%
Missing1731
Missing (%)3.8%
Memory size356.3 KiB
Minimum2025-01-27 00:00:00
Maximum2025-01-27 23:55:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-27T21:54:15.313241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:15.441691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Weatherconditions
Categorical

High correlation  Missing 

Distinct6
Distinct (%)< 0.1%
Missing616
Missing (%)1.4%
Memory size2.7 MiB
Fog
7654 
Stormy
7586 
Cloudy
7536 
Sandstorms
7495 
Windy
7422 

Length

Max length10
Median length6
Mean length5.8290682
Min length3

Characters and Unicode

Total characters262174
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunny
2nd rowStormy
3rd rowSandstorms
4th rowSunny
5th rowCloudy

Common Values

ValueCountFrequency (%)
Fog 7654
16.8%
Stormy 7586
16.6%
Cloudy 7536
16.5%
Sandstorms 7495
16.4%
Windy 7422
16.3%
Sunny 7284
16.0%
(Missing) 616
 
1.4%

Length

2025-01-27T21:54:15.573854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T21:54:15.670702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fog 7654
17.0%
stormy 7586
16.9%
cloudy 7536
16.8%
sandstorms 7495
16.7%
windy 7422
16.5%
sunny 7284
16.2%

Most occurring characters

ValueCountFrequency (%)
o 30271
11.5%
y 29828
11.4%
n 29485
11.2%
d 22453
8.6%
S 22365
8.5%
t 15081
 
5.8%
r 15081
 
5.8%
m 15081
 
5.8%
s 14990
 
5.7%
u 14820
 
5.7%
Other values (7) 52719
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 262174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 30271
11.5%
y 29828
11.4%
n 29485
11.2%
d 22453
8.6%
S 22365
8.5%
t 15081
 
5.8%
r 15081
 
5.8%
m 15081
 
5.8%
s 14990
 
5.7%
u 14820
 
5.7%
Other values (7) 52719
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 262174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 30271
11.5%
y 29828
11.4%
n 29485
11.2%
d 22453
8.6%
S 22365
8.5%
t 15081
 
5.8%
r 15081
 
5.8%
m 15081
 
5.8%
s 14990
 
5.7%
u 14820
 
5.7%
Other values (7) 52719
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 262174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 30271
11.5%
y 29828
11.4%
n 29485
11.2%
d 22453
8.6%
S 22365
8.5%
t 15081
 
5.8%
r 15081
 
5.8%
m 15081
 
5.8%
s 14990
 
5.7%
u 14820
 
5.7%
Other values (7) 52719
20.1%

Road_traffic_density
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing601
Missing (%)1.3%
Memory size2.6 MiB
Low
15477 
Jam
14143 
Medium
10947 
High
4425 

Length

Max length6
Median length3
Mean length3.8282806
Min length3

Characters and Unicode

Total characters172242
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowJam
3rd rowLow
4th rowMedium
5th rowHigh

Common Values

ValueCountFrequency (%)
Low 15477
33.9%
Jam 14143
31.0%
Medium 10947
24.0%
High 4425
 
9.7%
(Missing) 601
 
1.3%

Length

2025-01-27T21:54:15.802709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T21:54:15.873936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 15477
34.4%
jam 14143
31.4%
medium 10947
24.3%
high 4425
 
9.8%

Most occurring characters

ValueCountFrequency (%)
m 25090
14.6%
L 15477
9.0%
o 15477
9.0%
w 15477
9.0%
i 15372
8.9%
J 14143
8.2%
a 14143
8.2%
M 10947
6.4%
e 10947
6.4%
d 10947
6.4%
Other values (4) 24222
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 172242
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 25090
14.6%
L 15477
9.0%
o 15477
9.0%
w 15477
9.0%
i 15372
8.9%
J 14143
8.2%
a 14143
8.2%
M 10947
6.4%
e 10947
6.4%
d 10947
6.4%
Other values (4) 24222
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 172242
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 25090
14.6%
L 15477
9.0%
o 15477
9.0%
w 15477
9.0%
i 15372
8.9%
J 14143
8.2%
a 14143
8.2%
M 10947
6.4%
e 10947
6.4%
d 10947
6.4%
Other values (4) 24222
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 172242
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 25090
14.6%
L 15477
9.0%
o 15477
9.0%
w 15477
9.0%
i 15372
8.9%
J 14143
8.2%
a 14143
8.2%
M 10947
6.4%
e 10947
6.4%
d 10947
6.4%
Other values (4) 24222
14.1%

Vehicle_condition
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2
15034 
1
15030 
0
15009 
3
 
520

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45593
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Length

2025-01-27T21:54:15.964906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T21:54:16.034418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15034
33.0%
1 15030
33.0%
0 15009
32.9%
3 520
 
1.1%

Type_of_vehicle
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
motorcycle
26435 
scooter
15276 
electric_scooter
3814 
bicycle
 
68

Length

Max length16
Median length10
Mean length9.4922905
Min length7

Characters and Unicode

Total characters432782
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmotorcycle
2nd rowscooter
3rd rowmotorcycle
4th rowmotorcycle
5th rowscooter

Common Values

ValueCountFrequency (%)
motorcycle 26435
58.0%
scooter 15276
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Length

2025-01-27T21:54:16.117687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T21:54:16.183742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 26435
58.0%
scooter 15276
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 91050
21.0%
c 79724
18.4%
e 53221
12.3%
t 49339
11.4%
r 49339
11.4%
l 30317
 
7.0%
y 26503
 
6.1%
m 26435
 
6.1%
s 19090
 
4.4%
i 3882
 
0.9%
Other values (2) 3882
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 432782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 91050
21.0%
c 79724
18.4%
e 53221
12.3%
t 49339
11.4%
r 49339
11.4%
l 30317
 
7.0%
y 26503
 
6.1%
m 26435
 
6.1%
s 19090
 
4.4%
i 3882
 
0.9%
Other values (2) 3882
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 432782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 91050
21.0%
c 79724
18.4%
e 53221
12.3%
t 49339
11.4%
r 49339
11.4%
l 30317
 
7.0%
y 26503
 
6.1%
m 26435
 
6.1%
s 19090
 
4.4%
i 3882
 
0.9%
Other values (2) 3882
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 432782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 91050
21.0%
c 79724
18.4%
e 53221
12.3%
t 49339
11.4%
r 49339
11.4%
l 30317
 
7.0%
y 26503
 
6.1%
m 26435
 
6.1%
s 19090
 
4.4%
i 3882
 
0.9%
Other values (2) 3882
 
0.9%

multiple_deliveries
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing993
Missing (%)2.2%
Memory size2.6 MiB
1.0
28159 
0.0
14095 
2.0
 
1985
3.0
 
361

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters133800
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 28159
61.8%
0.0 14095
30.9%
2.0 1985
 
4.4%
3.0 361
 
0.8%
(Missing) 993
 
2.2%

Length

2025-01-27T21:54:16.276866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T21:54:16.351434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28159
63.1%
0.0 14095
31.6%
2.0 1985
 
4.5%
3.0 361
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 58695
43.9%
. 44600
33.3%
1 28159
21.0%
2 1985
 
1.5%
3 361
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 58695
43.9%
. 44600
33.3%
1 28159
21.0%
2 1985
 
1.5%
3 361
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 58695
43.9%
. 44600
33.3%
1 28159
21.0%
2 1985
 
1.5%
3 361
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 58695
43.9%
. 44600
33.3%
1 28159
21.0%
2 1985
 
1.5%
3 361
 
0.3%

City
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing1200
Missing (%)2.6%
Memory size3.0 MiB
Metropolitian
34093 
Urban
10136 
Semi-Urban
 
164

Length

Max length13
Median length13
Mean length11.162323
Min length5

Characters and Unicode

Total characters495529
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowMetropolitian
3rd rowUrban
4th rowMetropolitian
5th rowMetropolitian

Common Values

ValueCountFrequency (%)
Metropolitian 34093
74.8%
Urban 10136
 
22.2%
Semi-Urban 164
 
0.4%
(Missing) 1200
 
2.6%

Length

2025-01-27T21:54:16.457450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T21:54:16.534761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian 34093
76.8%
urban 10136
 
22.8%
semi-urban 164
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 68350
13.8%
t 68186
13.8%
o 68186
13.8%
r 44393
9.0%
a 44393
9.0%
n 44393
9.0%
e 34257
6.9%
M 34093
6.9%
p 34093
6.9%
l 34093
6.9%
Other values (5) 21092
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 495529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 68350
13.8%
t 68186
13.8%
o 68186
13.8%
r 44393
9.0%
a 44393
9.0%
n 44393
9.0%
e 34257
6.9%
M 34093
6.9%
p 34093
6.9%
l 34093
6.9%
Other values (5) 21092
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 495529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 68350
13.8%
t 68186
13.8%
o 68186
13.8%
r 44393
9.0%
a 44393
9.0%
n 44393
9.0%
e 34257
6.9%
M 34093
6.9%
p 34093
6.9%
l 34093
6.9%
Other values (5) 21092
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 495529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 68350
13.8%
t 68186
13.8%
o 68186
13.8%
r 44393
9.0%
a 44393
9.0%
n 44393
9.0%
e 34257
6.9%
M 34093
6.9%
p 34093
6.9%
l 34093
6.9%
Other values (5) 21092
 
4.3%

Temperature
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.57647
Minimum16
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:16.616073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile16
Q120
median23
Q330
95-th percentile36
Maximum36
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9805969
Coefficient of variation (CV)0.24334646
Kurtosis-0.96419785
Mean24.57647
Median Absolute Deviation (MAD)4
Skewness0.49165885
Sum1120515
Variance35.767539
MonotonicityNot monotonic
2025-01-27T21:54:16.716064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
23 5051
 
11.1%
18 4203
 
9.2%
20 4175
 
9.2%
19 3332
 
7.3%
21 3217
 
7.1%
26 2613
 
5.7%
36 2609
 
5.7%
31 2475
 
5.4%
25 2442
 
5.4%
16 2429
 
5.3%
Other values (9) 13047
28.6%
ValueCountFrequency (%)
16 2429
5.3%
17 776
 
1.7%
18 4203
9.2%
19 3332
7.3%
20 4175
9.2%
21 3217
7.1%
22 1831
 
4.0%
23 5051
11.1%
24 745
 
1.6%
25 2442
5.4%
ValueCountFrequency (%)
36 2609
5.7%
35 1786
3.9%
34 701
 
1.5%
33 2399
5.3%
31 2475
5.4%
30 1785
3.9%
29 735
 
1.6%
28 2289
5.0%
26 2613
5.7%
25 2442
5.4%

Traffic_Index
Real number (ℝ)

High correlation 

Distinct177
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4640672
Minimum0.16666667
Maximum6.5333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:16.858889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.16666667
5-th percentile0.28333333
Q10.5
median1.0666667
Q32.0666667
95-th percentile3.8666667
Maximum6.5333333
Range6.3666667
Interquartile range (IQR)1.5666667

Descriptive statistics

Standard deviation1.2064471
Coefficient of variation (CV)0.82403807
Kurtosis1.8987782
Mean1.4640672
Median Absolute Deviation (MAD)0.66666667
Skewness1.4066411
Sum66751.217
Variance1.4555147
MonotonicityNot monotonic
2025-01-27T21:54:16.986684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 966
 
2.1%
0.3333333333 944
 
2.1%
0.3666666667 919
 
2.0%
0.5 761
 
1.7%
1 754
 
1.7%
0.4666666667 749
 
1.6%
0.4333333333 737
 
1.6%
0.6 725
 
1.6%
0.8666666667 712
 
1.6%
0.5666666667 686
 
1.5%
Other values (167) 37640
82.6%
ValueCountFrequency (%)
0.1666666667 233
 
0.5%
0.1833333333 221
 
0.5%
0.2 221
 
0.5%
0.2166666667 201
 
0.4%
0.2222222222 7
 
< 0.1%
0.2333333333 237
0.5%
0.2444444444 8
 
< 0.1%
0.25 585
1.3%
0.2666666667 564
1.2%
0.2833333333 563
1.2%
ValueCountFrequency (%)
6.533333333 36
 
0.1%
6.4 37
 
0.1%
6.266666667 32
 
0.1%
6.133333333 31
 
0.1%
6 32
 
0.1%
5.866666667 103
0.2%
5.733333333 116
0.3%
5.6 98
0.2%
5.466666667 120
0.3%
5.333333333 113
0.2%

Time_taken
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.294607
Minimum10
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-01-27T21:54:17.128295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median26
Q332
95-th percentile44
Maximum54
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3838061
Coefficient of variation (CV)0.3568719
Kurtosis-0.31079787
Mean26.294607
Median Absolute Deviation (MAD)7
Skewness0.48595123
Sum1198850
Variance88.055818
MonotonicityNot monotonic
2025-01-27T21:54:17.247070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26 2123
 
4.7%
25 2050
 
4.5%
27 1976
 
4.3%
28 1965
 
4.3%
29 1956
 
4.3%
19 1824
 
4.0%
15 1810
 
4.0%
18 1765
 
3.9%
16 1706
 
3.7%
17 1696
 
3.7%
Other values (35) 26722
58.6%
ValueCountFrequency (%)
10 750
1.6%
11 757
1.7%
12 746
1.6%
13 716
 
1.6%
14 739
1.6%
15 1810
4.0%
16 1706
3.7%
17 1696
3.7%
18 1765
3.9%
19 1824
4.0%
ValueCountFrequency (%)
54 91
 
0.2%
53 100
 
0.2%
52 79
 
0.2%
51 94
 
0.2%
50 72
 
0.2%
49 280
0.6%
48 277
0.6%
47 295
0.6%
46 274
0.6%
45 241
0.5%

Interactions

2025-01-27T21:54:09.936498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:01.324225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:02.440426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:03.432104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:04.380137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:05.342462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:06.470139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:07.523635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:08.719722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:10.055263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:01.456793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:02.554969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:03.544169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:04.499373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:05.449915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:06.587679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:07.663495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:08.830762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:10.157810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:01.567623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:02.658998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:03.637413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:04.600917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:05.548933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:06.686224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-27T21:54:09.427108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:10.628602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:01.998333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:03.005230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-27T21:54:09.524284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-01-27T21:54:07.401921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:08.611709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T21:54:09.840396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-27T21:54:17.356233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CityDelivery_location_latitudeDelivery_location_longitudeDelivery_person_AgeDelivery_person_RatingsRoad_traffic_densityTemperatureTime_takenTraffic_IndexType_of_vehicleVehicle_conditionWeatherconditionsmultiple_deliveriestranslogi_latitudetranslogi_longitude
City1.0000.0010.0010.0620.0490.0750.0570.2780.1390.0360.0630.0370.1300.0000.004
Delivery_location_latitude0.0011.0000.1220.004-0.0100.018-0.0030.0300.0300.0110.0000.0000.0090.9730.116
Delivery_location_longitude0.0010.1221.0000.008-0.0080.0010.0010.0280.0240.0090.0000.0000.0040.1110.988
Delivery_person_Age0.0620.0040.0081.000-0.0960.0000.0080.3110.1330.2430.5770.0070.0790.0030.006
Delivery_person_Ratings0.049-0.010-0.008-0.0961.0000.0780.035-0.294-0.1490.2470.5820.0820.091-0.007-0.004
Road_traffic_density0.0750.0180.0010.0000.0781.0000.5060.2650.5740.0000.0060.0000.1090.0060.002
Temperature0.057-0.0030.0010.0080.0350.5061.000-0.227-0.2620.0670.1750.7990.087-0.0020.003
Time_taken0.2780.0300.0280.311-0.2940.265-0.2271.0000.6610.1050.1820.1390.3370.0150.009
Traffic_Index0.1390.0300.0240.133-0.1490.574-0.2620.6611.0000.0760.2350.0930.1960.0200.011
Type_of_vehicle0.0360.0110.0090.2430.2470.0000.0670.1050.0761.0000.4570.0000.0470.0650.109
Vehicle_condition0.0630.0000.0000.5770.5820.0060.1750.1820.2350.4571.0000.0000.0750.1640.251
Weatherconditions0.0370.0000.0000.0070.0820.0000.7990.1390.0930.0000.0001.0000.0680.0000.000
multiple_deliveries0.1300.0090.0040.0790.0910.1090.0870.3370.1960.0470.0750.0681.0000.0110.000
translogi_latitude0.0000.9730.1110.003-0.0070.006-0.0020.0150.0200.0650.1640.0000.0111.0000.122
translogi_longitude0.0040.1160.9880.006-0.0040.0020.0030.0090.0110.1090.2510.0000.0000.1221.000

Missing values

2025-01-27T21:54:11.076300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-27T21:54:11.302701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-27T21:54:11.623521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDDelivery_person_AgeDelivery_person_Ratingstranslogi_latitudetranslogi_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdWeatherconditionsRoad_traffic_densityVehicle_conditionType_of_vehiclemultiple_deliveriesCityTemperatureTraffic_IndexTime_taken
00x460737.04.922.74504975.89247122.76504975.9124712022-03-1911:30:00SunnyHigh2motorcycle0.0Urban29.01.20000024
10xb37934.04.512.91304177.68323713.04304177.8132372022-03-2519:45:00StormyJam2scooter1.0Metropolitian20.02.20000033
20x5d6d23.04.412.91426477.67840012.92426477.6884002022-03-1908:30:00SandstormsLow0motorcycle1.0Urban36.00.43333326
30x7a6a38.04.711.00366976.97649411.05366977.0264942022-04-0518:00:00SunnyMedium0motorcycle1.0Metropolitian30.00.70000021
40x70a232.04.612.97279380.24998213.01279380.2899822022-03-2613:30:00CloudyHigh1scooter1.0Metropolitian24.03.00000030
50x9bb422.04.817.43166878.40832117.46166878.4383212022-03-1121:20:00CloudyJam0motorcycle1.0Urban23.01.73333326
60x95b433.04.723.36974685.33982023.47974685.4498202022-03-0419:15:00FogJam1scooter1.0Metropolitian16.05.33333340
70x9eb235.04.612.35205876.60665012.48205876.7366502022-03-1417:25:00CloudyMedium2motorcycle1.0Metropolitian25.01.06666732
80x110222.04.817.43380978.38674417.56380978.5167442022-03-2020:55:00StormyJam0motorcycle1.0Metropolitian20.02.26666734
90xcdcd36.04.230.32796878.04610630.39796878.1161062022-02-1221:55:00FogJam2motorcycle3.0Metropolitian16.03.06666746
IDDelivery_person_AgeDelivery_person_Ratingstranslogi_latitudetranslogi_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdWeatherconditionsRoad_traffic_densityVehicle_conditionType_of_vehiclemultiple_deliveriesCityTemperatureTraffic_IndexTime_taken
455830x519336.04.812.31097276.65926412.44097276.7892642022-03-1821:10:00SunnyJam2electric_scooter1.0Urban28.01.93333329
455840xa33337.04.813.02239480.24243913.04239480.2624392022-04-0509:35:00SandstormsLow2electric_scooter0.0Metropolitian36.00.33333320
455850xc9ab30.04.226.46900380.31634426.53900380.3863442022-02-1418:10:00CloudyMedium1motorcycle2.0Metropolitian25.02.80000042
455860x4e2128.04.913.02919877.57099713.05919877.6009972022-03-3021:55:00SandstormsJam1scooter1.0Metropolitian33.03.86666729
455870x117835.04.223.37129285.32787223.48129285.4378722022-03-0821:45:00WindyJam2motorcycle1.0Metropolitian18.02.20000033
455880x7c0930.04.826.90232875.79425726.91232875.8042572022-03-2411:35:00WindyHigh1motorcycle0.0Metropolitian19.03.20000032
455890xd64121.04.60.0000000.0000000.0700000.0700002022-02-1619:55:00WindyJam0motorcycle1.0Metropolitian18.02.40000036
455900x4f8d30.04.913.02239480.24243913.05239480.2724392022-03-1123:50:00CloudyLow1scooter0.0Metropolitian26.00.53333316
455910x5eee20.04.711.00175376.98624111.04175377.0262412022-03-0713:35:00CloudyHigh0motorcycle1.0Metropolitian24.01.30000026
455920x5fb223.04.923.35105885.32573123.43105885.4057312022-03-0217:10:00FogMedium2scooter1.0Metropolitian18.01.20000036